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Acquisition of motor skills often involves the concatenation of single movements into sequences. Along the course of learning, sequential performance becomes progressively faster and smoother, presumably by optimization of both motor planning and motor execution. Following its encoding during training, “how-to” memory undergoes consolidation, reflecting transformations in performance and its neurobiological underpinnings over time. This offline post-training memory process is characterized by two phenomena: reduced sensitivity to interference and the emergence of delayed, typically overnight, gains in performance. Successful learning is a result of strict control (gating) over the on-line and off-line stages of the experience-driven changes in the brain’s organization (neural plasticity). Factors, such as the amount of practice, the passage of time and the affordance of sleep and factors specific to the learning environment may selectively affect, – block or accelerate, - the expression of delayed gains in motor performance. These factors interact in a complex, non-linear manner. Developmental and inter-individual differences impose additional constraints on memory processes (e.g., age, chronotype, clinical condition).
High-level reorganization of the movements as a unit following practice was shown to be subserved by optimization of planning and execution of individual movements. Temporal and kinematic analysis of performance demonstrated that only the offline inter-movement interval shortening (co-articulation) is selectively blocked by the interference experience, while velocity and amplitude, comprising movement time, are interference–insensitive. Sleep, including a day-time sleep, reduces the susceptibility of the memory trace to retroactive behavioural interference and also accelerates the expression of delayed gains in performance. Activity in cortico-striatal areas that was disrupted during the day due to interference and accentuated in the absence of a day-time sleep is restored overnight. Additional line of experiments showed that on-line environmental noise during training (vibro-auditory task-irrelevant stimulation) may be an important modulator of memory consolidation; its impact is ambiguous, presumably contingent on baseline arousal levels of the individual.

1. Albouy G., King B. R., Schmidt C., Desseilles M., Dang-Vu T., Balteau E., Phillips C., Degueldre C., Orban P., Benali H., Peigneux P., Luxen A., Karni A., Doyon J., Maquet P., Korman M. 2016 Cerebral Activity Associated with Transient Sleep-Facilitated Reduction in Motor Memory Vulnerability to Interference Scientific Reports 6:34948
2. Friedman J., Korman M. 2016 Offline optimization of the relative timing of movements in a sequence is blocked by behavioral retroactive interference Frontiers in Human Neuroscience, 10:623
3. Korman M., Herling Z., Levy I., Egbarieh N., Engel-Yeger B., Karni A. 2017 Background matters: minor vibratory sensory stimulation during motor skill acquisition selectively reduces off-line memory consolidation. Neurobiology of Learning and Memory 140:27-32

A-T-Al aluminides (where A = actinide/lanthanide/rare earth elements and T=transition metal) were intensively studied due to their ability to form heavy fermion compounds that could possess unique physical properties [1-3, for example]. Although A-T-Al family contains hundreds of phases, they can be classified into only a few series of phases with isotypical structures. Al richest are: tetragonal ATxAl12-x (ThMn12 type), tetragonal AT2Al10 (CaCr2Al10 type), orthorhombic AT2Al10 (YbFe2Al10 type) and cubic AT2Al20 (CeCr2Al20 type). Due to the intimate link between structure and properties, in order to understand and enhance physical properties – exact atomic structure of these materials should be known. Such researches are performed usually using “trial and error” approach, e.g. cast and characterize, which could be time consuming. It would be of clear benefit to formulate a rule that could predict the relative stability of the structures that may form in the ternary Al-richest phases in the A-T-Al systems.
Current research was conducted with an aim to understand the influence of A and T atom types on the formation of the stable structures in the AT2Al20 alloys. The work was performed systematically, investigating several AT2Al20 alloys both experimentally and by Density Functional theory (DFT) calculations. Study on the ThT2Al20 systems (where T=Ti, V, Cr, Mn and Fe) was previously performed by our group suggesting that the magnetic moment of T atoms can be used as a good descriptor of phase stability [4-5]. Now, we focus on the investigation of the AMn2Al20, where A elements were selected according to their electronic structure. Theoretical and experimental results were found to be in perfect agreement. By analyzing the density of states (DOS) we found that the different behavior of the 4f and 5f-shell electrons of the heavy atom, eventually determines which structure will be favorable [6].
While studying these A-T-Al systems new unknown ternary phases were discovered: Th2Ni10Al15 [7] and Nd2Re3Al15. Since in both cases the alloys of an interest did not attain equilibrium state despite the prolonged heat treatments - they contained multiple phases. Therefore, electron crystallography methods were the only viable tool applicable for structure solution of these phases. In current research, electron diffraction tomography (EDT) approach was successfully used for solution of atomic structure of both phases.

Boundary layers control the transport of momentum, heat, solutes and other quantities between walls and the bulk of a flow. The Prandtl-Blasius boundary layer was the first quantitative example of a flow profile near a wall and could be derived by an asymptotic expansion of the Navier-Stokes equation. For higher flow speeds we have scaling arguments and models, but no derivation from the Navier-Stokes equation.
The analysis of exact coherent structures in plane Couette flow reveals ingredients of such a more rigorous description of boundary layers. I will describe how exact coherent structures can be scaled to obtain self-similar structures on ever smaller scales as the Reynolds number increases.
A quasilinear approximation allows to combine the structures self-consistently to form boundary layers. Going beyond the quasilinear approximation will then open up new approaches for controlling and manipulating boundary layers.